Adjustable speed induction motors are widely used due to low maintenance cost and acceptable performance. The speed control design, or equivalently, the motor drive design, of the induction motor is challenging due to highly nonlinear dynamics. Among various means, the vector (field oriented) control appears to be a good and most popular solution that has evolved as a mature technology.
This invention considers speed sensorless control technologies for the induction motor, i.e., an inductor motor where the rotor speed or position is not measured by encoders. Speed sensorless motor drives (motor drives) are desirable due to the elimination of speed sensors, lower cost, and improved reliability of the resultant system. Current speed sensorless motor drives suffer significant performance degradation due to the absence of the encoder to sense the rotor position and speed. Thus, their applications remain limited to fields requiring low or medium performance.
The performance of speed sensorless motor drives relies heavily on the performance of a flux and speed estimator 106-107 described below with reference to FIGS. 1A and 1B. A number of prior arts methods contribute to the developments of speed sensorless control technologies. The flux and speed estimator design methods include: a voltage model based direct integration approach, an adaptive observer approach, an extended kalman filter approach, see Schauder, “Adaptive speed identification for vector control of induction motors without rotational transducers,” IEEE Transactions on Industry Applications, vol. 28, no. 5, pp. 1054-1061, September/October 1992, H. Kubota, K. Matsuse, and T. Nakano, “DSP-based speed adaptive flux observer of induction motor,” IEEE Transactions on Industry Applications, vol. 29, no. 2, pp. 344-348, March/April 1993, H. Kubota and K. Matsuse, “Speed sensorless field-oriented control of induction motor with rotor resistance adaption,” IEEE Transactions on Industry Applications, vol. 30, no. 5, pp. 1219-1224, September/October 1994, K. Ohyama, G. M. Asher, and M. Sumner, “Comparative analysis of experimental performance and stability of sensorless induction motor drives,” IEEE Transactions on Industrial Electronics, vol. 53, no. 1, pp. 178-186, February 2006, D. J. Atkinson, P. P. Acamley, and J. W. Finch, “Observers for induction motor state and parameter estimation,” IEEE Transactions on Industry Applications, vol. 27, no. 6, pp. 1119-1127, November/December 1991, and M. Hilairet, F. Auger, and E. Berthelot, “Speed and rotor flux estimation of induction machines using a two-stage extended kalman filter,” Automatica, vol. 45, no. 8, pp. 1819-1827, August 2009.
FIGS. 1A and 1B show a conventional speed sensorless motor 105 and controller. An input signal 111 is a reference rotor flux amplitude, a signal 112 is an estimate outputted from a flux estimator block 106. A signal 113 represents a difference between signals 111 and 112.
A flux control block 101 determines a stator current in the d-axis, denoted by signal 114. A signal 115, as an estimate or true stator current in the d-axis, is produced by the flux estimator 106.
A difference signal 116 between signals 115 and 114 is used by a current control block 103 to determine a reference stator voltage in the d-axis, which is part of signal 123. Similarly, a signal 117 denotes a desired rotor speed reference for the induction motor. A signal 118 denotes an estimated rotor speed produced by a speed estimator block 107 based on an output signal 126 of the flux estimator 106.
A difference signal 119 between signals 117 and 118 is input to the speed control block 102 to determine a reference stator current 120 in the q-axis. An estimated or true stator current 121 in the q-axis is compared to the reference stator current 120 in the q-axis to produce a difference signal 122. The current control block 103 determines a desired stator voltage signal 123 in d- and q-axis, on the basis of the difference signals 116 and 122.
An inverse Clarke/Park transformation 104 convert the desired stator voltages signals in d- and q-axis into desired well-known three-phase voltages, and produces three-phase voltages, denoted as by 124, to drive the induction motor 105.
Note that the flux estimator 106 takes the three-phase voltages 124, sensed 131 phase currents 125, see FIG. 1B, as input signals and outputs estimated or true stator currents 115 and 121, estimated rotor flux amplitude 112, and estimated rotor speed signal 118 to produce the difference signals 113, 116, 119, and 122. The signal 119 is used by the speed control block 102.
FIGS. 2A and 2B show prior art estimator methods based on stator currents and voltages 211, which are measured by sensors 202 and are assumed in balanced three-phases and in orthogonal stationary frame, and an induction motor model 201. A Clarke/Park transformation 203 is applied to transform the induction motor model 201 and the sensed signals 211 so that quantities (including variables in the induction motor model and measured signals) in balanced three-phases are converted into quantities in balanced two-phases.
All these estimation methods fit an architecture shown in FIGS. 2A and 2B. As shown in FIG. 2A-2B, the estimator methods are based on stator currents and voltages signals, denoted by 211, measured by a sensing block 202 and an induction model 201. A Park Transformation block 203 may or may not be used to transform the induction motor model 201 and the sensed signals 211. A block 204 represents an estimator to produce estimates of stator currents, rotor flux, and rotor speed signals. The voltage model based direct integration suffers accumulation error due to inaccurate measurement. Adaptive observer and extended kalman filter approaches yield slow speed tracking performance because the speed is treated as an unknown parameter and its identification is slow.
This fact is elaborated by FIG. 2B, where block 222 represents the unnecessary assumption that the rotor speed is a parameter, and a speed estimator 223 produces the rotor speed estimate based on outputs of block 221, and the assumption 222.
Overall, most of existing speed sensorless motor drives produce limited speed tracking performance due to performing estimator design in appropriate state coordinates and under unnecessary assumption. Also, most speed-sensorless estimation methods are ad-hoc and the resultant estimation error dynamics are not guaranteed to be stable.